Arindam Biswas

When AI Learns to Improve How It Improves

Key takeaway: Hyperagents extend self-improving AI beyond coding domains by making the meta-level improvement mechanism itself modifiable. The DGM-Hyperagents (DGM-H) system improved performance on paper review from 0.0 to 0.710, on robotics reward design from 0.060 to 0.372, and transferred self-improvement

Why We Can't Prove AI Self-Improvement—and Why It Might Still Work

The Darwin Gödel Machine (DGM) is a self-improving system that iteratively modifies its own code, evaluates each change empirically on coding benchmarks, and maintains an archive of diverse agent versions. It improved performance from 20.0% to 50.0% on SWE-bench and from 14.2% to 30.7%